The number of samples when the imbalance degree is 0.9.

<p>The number of samples when the imbalance degree is 0.9.</p>

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Bibliographic Details
Main Author:	Xin Liu (43569) (author)
Other Authors:	Han Wang (254423) (author), Zhiyong Du (520314) (author), Xu Xu (210339) (author), Bo Song (255492) (author)
Published:	2025
Subjects:	Infectious Diseases Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified synthesize realistic minority subsequently classified using stationary fault features rolling bearing faults robust feature extraction reliable fault diagnosis raw vibration signals lightweight residual network lightweight diagnosis framework learning statistical distributions dimensional latent space deep learning models cwru benchmark demonstrate based generative model proposed approach offers pfrnet outperforms state based data augmentation severe class imbalance proposed method propose pfrnet class samples safe operation rotating machinery real data quantitative evaluations promising solution poisson flow mitigating imbalance minimal complexity diagnostic accuracy capture non augmented datasets art methods
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